Surbhi Goel

Surbhi Goel

Postdoc Researcher

Microsoft Research, New York City

I am a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group.

My research interests lie at the intersection of theoretical computer science and machine learning, with a focus on developing theoretical foundations for modern machine learning paradigms including deep learning. My work attempts to quantify the limitations of existing approaches and design new efficient algorithms with provable guarantees.

Prior to joining MSR, I obtained my Ph.D. in the Computer Science department at the University of Texas at Austin advised by Adam Klivans. My dissertation was awarded UTCS’s Bert Kay Dissertation award. My research was generously supported by the JP Morgan AI Fellowship and several fellowships from UT Austin. During my PhD, I visited IAS for the Theoretical Machine learning program and the Simons Institute for the Theory of Computing at UC Berkeley for the Foundations of Deep Learning program (supported by the Simons-Berkeley Research Fellowship). Before that, I received my Bachelors degree from Indian Institute of Technology (IIT) Delhi majoring in Computer Science and Engineering.

I am on the job market for positions starting in Fall 2022.

Download my resumé.

Interests
  • Theory
  • Machine Learning
Education
  • PhD in Computer Science, 2020

    University of Texas at Austin

  • MS in Computer Science, 2019

    University of Texas at Austin

  • BTech in Computer Science and Engineering, 2015

    Indian Institute of Technology, Delhi

Recent & Upcoming Talks

Recent Publications & Preprints

Inductive Biases and Variable Creation in Self-Attention Mechanisms
Gone Fishing: Neural Active Learning with Fisher Embeddings
Investigating the Role of Negatives in Contrastive Representation Learning
Acceleration via Fractal Learning Rate Schedules
Statistical Estimation from Dependent Data

Outreach

Co-organizer
Co-founded this mentorship initiative for the learning theory community. Co-organized mentorship workshops at ALT 2021 and COLT 2021.

Professional Services

Program Committee
Virtual Experience Chair
Program Committee
Program Committee